Technical Field
Embodiments of the subject matter disclosed herein generally relate to methods and systems and, more particularly, to mechanisms and techniques for providing high-quality images of a surveyed subsurface, with balanced amplitudes, improved focusing and enhanced resolution.
Discussion of the Background
Marine seismic data acquisition and processing generate a profile (image) of the geophysical structure under the seafloor. While this profile does not provide an accurate location for oil and gas reservoirs, it suggests, to those trained in the field, the presence or absence of them. Thus, providing a high-resolution image of the structures under the seafloor is an ongoing process.
During a seismic gathering process, as shown in FIG. 1, a vessel 100 tows an array of seismic detectors 112 provided along cables 114. The detectors and cables form streamers 116. Streamers 116 may be disposed horizontally, i.e., lie at a constant depth z1 relative to the surface 118 of the ocean, or they may be tilted in relation to this surface. Vessel 100 also tows a sound source 120 configured to generate an acoustic wave 122a. Acoustic wave 122a propagates downward, toward seafloor 124, and penetrates it until eventually a reflecting structure 126 (reflector) reflects the acoustic wave. The reflected acoustic wave 122b propagates upward until it is detected by detector 112. The recorded data is then processed to produce an accurate image of the subsurface.
Processing includes various phases, e.g., velocity model determination, pre-stack, migration, post-stack, etc., which are known in the art, so their description is omitted herein. Progress in pre-stack depth imaging has been considerable in the past. Theoretical progress has provided better methods for extrapolating wavefields measured at the earth's surface into the subsurface, and practical progress has linked the migrations more closely with velocity model-building and interpretation. Migration is the process of propagating, for example, a wavefield measured at a receiver location to a reflector located in the subsurface. Migration may also be applied to wavefields generated by a source.
Initial migration methods (Kirchhoff and beam) relied on rays to approximate the Green's functions for wave propagation. More accurate methods rely on the so-called wave-equation migration algorithms that use full waveform Green's functions. The most computationally efficient algorithms for doing this are collectively called one-way wave equation migration (OWEM). These algorithms decompose seismic wavefields inside the earth into up-going waves and down-going waves under the assumption of no interaction between these two wavefields; that is, no turning wave and vertical reflection generation during the synthesis of wave propagation.
Use of the two-way wave equation in depth migration began some time ago in an algorithm called reverse-time migration (RTM), see, for example, Zhang and Sun, 2009, “Practical issues of reverse time migration: true-amplitude gathers, noise removal and harmonic-source encoding,” First Break, Vol. 26, 19-25, and Xu et al., 2011, “3D angle gathers from reverse time migration,” Geophysics, Vol. 76, No. 2, S77-S92, the entire contents of which are incorporated herein by reference.
However, this approach was limited due to its need for computer power. With increases in computer power, RTM has developed rapidly over the last few years, and theoretical advantages such as dip-unlimited accurate wave propagation and improved amplitudes have provided imaging benefits in practice. For example, in complex subsalt and salt flank areas, the numerical Green's functions from finite difference to the two-way wave equation have better amplitude behavior, so it is easier to incorporate amplitude corrections into RTM than into OWEM. In addition to its ability to handle complex velocities distributions, many current RTM algorithms can handle anisotropic media such as vertical transverse isotropy (VTI) and tilted transverse isotropy (TTI).
One of the most common RTM imaging conditions is cross-correlation of the forward propagated source wavefield with the backward propagated receiver wavefield. However, the image contains amplitude distortions caused by RTM crosstalk artifacts. An inversion-based least-squares migration (LSM) scheme has been shown to noticeably reduce migration artifacts and improve lateral spatial resolution. LSM iteratively seeks a final image which best matches the amplitude of simulated and recorded seismic data. This final image is thus called the inverted image. However, the forward modeling and migration engine for the LSM scheme were in the past the Kirchhoff migration, which suffers from the limitations noted above. Recently, one-wave wave equation and two-way RTM were used as the modeling and migration engine.
More recently, true-amplitude RTM became the state-of-the-art technology for imaging and interpreting subtle and complex geologic features, while its conjugate process, reverse-time demigration (RTDM) (see, for example, Zhang and Duan, 2012, “Predicting multiples using a reverse time demigration,” 83rd Annual International Meeting, SEG, Expanded Abstracts, 520-524, the entire content of which is incorporated herein by reference), has become a novel technique to predict primary, interbed and surface-related multiples and free surface ghosts for a variety of acquisition geometries. Demigration is considered the inverse of the adjoint process of migration. Demigration uses reflectivity to predict seismic data. Both techniques, RTM and RTDM, take advantage of a complete set of acoustic waves (reflections, transmissions, diffractions, prismatic waves, etc.), correctly handle complex velocities, and propagate waves without angle limitations. For these reasons, RTM and RTDM are desirable as the imaging and modeling operators in least-squares RTM (LSRTM). The modeling uses velocity and density as the input models.
However, it is not an easy task to directly apply conventional amplitude-matching-based LSRTM to real datasets because the earth is a visco-elastic medium with density variations, much more complicated than the models used to propagate acoustic wavefields in seismic imaging. As a result, amplitude matching is never perfect. Also, it is difficult to define a good source signature in the modeling. The challenge of determining source strength, which can vary from source to source, is even greater. All these practical issues require considerable effort in preprocessing both the observed and simulated data to correctly use the conventional LSRTM formulation.
Thus, it is desirable to introduce a new general framework of LSRTM that avoids the above-noted problems and relaxes the amplitude constraints of existing LSRTM.